Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques
ABSTRACT Background and Aims Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. This study aims to predict myopia among undergraduate students using ensemble machine learning techniques and to identify key risk factors associated with i...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70874 |
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| author | Isteaq Kabir Sifat Tajin Ahmed Jisa Jyoti Shree Roy Nourin Sultana Farhana Hasan Md Parvez Mosharaf Md. Kaderi Kibria |
| author_facet | Isteaq Kabir Sifat Tajin Ahmed Jisa Jyoti Shree Roy Nourin Sultana Farhana Hasan Md Parvez Mosharaf Md. Kaderi Kibria |
| author_sort | Isteaq Kabir Sifat |
| collection | DOAJ |
| description | ABSTRACT Background and Aims Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. This study aims to predict myopia among undergraduate students using ensemble machine learning techniques and to identify key risk factors associated with its development. Methods A cross‐sectional study was conducted in Dinajpur city, collecting 514 samples through a self‐structured questionnaire covering demographic information, myopia prevalence and risk factors, knowledge and attitudes, and daily activities. Four feature selection techniques Boruta‐based feature selection (BFS), Least Absolute Shrinkage and Selection Operator regression, Forward and Backward Selection and Random Forest (RF) identified 12 key predictive features. Using these features, ensemble methods, including logistic regression artificial neural network, RF, Support Vector Machine, extreme gradient boosting, and light gradient boosting machine were employed for prediction. Model performance was evaluated using accuracy, precision, recall, F1‐score, and area under the curve (AUC). Results The stacking ensemble model achieved the highest performance, with an accuracy of 95.42%, recall of 93.42%, precision of 98.85%, F1‐score of 96.08%, and AUC of 0.979. SHapley Additive exPlanations analysis identified key risk factors, including visual impairment, family history of myopia, excessive screen time, and insufficient outdoor activities. Conclusion These findings demonstrate the effectiveness of ensemble machine learning in predicting myopia and highlight the potential for early intervention strategies. By identifying high‐risk individuals, targeted awareness programs and lifestyle modifications can help mitigate myopia progression among undergraduate students. |
| format | Article |
| id | doaj-art-852ee1c4be5b42b89e6d5fa6d1283f49 |
| institution | Kabale University |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-852ee1c4be5b42b89e6d5fa6d1283f492025-08-20T03:59:36ZengWileyHealth Science Reports2398-88352025-05-0185n/an/a10.1002/hsr2.70874Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning TechniquesIsteaq Kabir Sifat0Tajin Ahmed Jisa1Jyoti Shree Roy2Nourin Sultana3Farhana Hasan4Md Parvez Mosharaf5Md. Kaderi Kibria6Department of Statistics Hajee Mohammad Danesh Science and Technology University Dinajpur Rangpur BangladeshDepartment of Statistics Hajee Mohammad Danesh Science and Technology University Dinajpur Rangpur BangladeshDepartment of Statistics Hajee Mohammad Danesh Science and Technology University Dinajpur Rangpur BangladeshDepartment of Statistics Hajee Mohammad Danesh Science and Technology University Dinajpur Rangpur BangladeshDepartment of Statistics University of Rajshahi Rajshahi BangladeshSchool of Business, Faculty of Business, Education, Law and Arts University of Southern Queensland Toowoomba Queensland AustraliaDepartment of Statistics Hajee Mohammad Danesh Science and Technology University Dinajpur Rangpur BangladeshABSTRACT Background and Aims Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. This study aims to predict myopia among undergraduate students using ensemble machine learning techniques and to identify key risk factors associated with its development. Methods A cross‐sectional study was conducted in Dinajpur city, collecting 514 samples through a self‐structured questionnaire covering demographic information, myopia prevalence and risk factors, knowledge and attitudes, and daily activities. Four feature selection techniques Boruta‐based feature selection (BFS), Least Absolute Shrinkage and Selection Operator regression, Forward and Backward Selection and Random Forest (RF) identified 12 key predictive features. Using these features, ensemble methods, including logistic regression artificial neural network, RF, Support Vector Machine, extreme gradient boosting, and light gradient boosting machine were employed for prediction. Model performance was evaluated using accuracy, precision, recall, F1‐score, and area under the curve (AUC). Results The stacking ensemble model achieved the highest performance, with an accuracy of 95.42%, recall of 93.42%, precision of 98.85%, F1‐score of 96.08%, and AUC of 0.979. SHapley Additive exPlanations analysis identified key risk factors, including visual impairment, family history of myopia, excessive screen time, and insufficient outdoor activities. Conclusion These findings demonstrate the effectiveness of ensemble machine learning in predicting myopia and highlight the potential for early intervention strategies. By identifying high‐risk individuals, targeted awareness programs and lifestyle modifications can help mitigate myopia progression among undergraduate students.https://doi.org/10.1002/hsr2.70874Dinajpur cityensemble machine learningMyopiarefractive errorSHAP analysisundergraduate students |
| spellingShingle | Isteaq Kabir Sifat Tajin Ahmed Jisa Jyoti Shree Roy Nourin Sultana Farhana Hasan Md Parvez Mosharaf Md. Kaderi Kibria Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques Health Science Reports Dinajpur city ensemble machine learning Myopia refractive error SHAP analysis undergraduate students |
| title | Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques |
| title_full | Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques |
| title_fullStr | Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques |
| title_full_unstemmed | Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques |
| title_short | Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques |
| title_sort | prediction of myopia among undergraduate students using ensemble machine learning techniques |
| topic | Dinajpur city ensemble machine learning Myopia refractive error SHAP analysis undergraduate students |
| url | https://doi.org/10.1002/hsr2.70874 |
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